Electronics case study: plan for ₹1L to ₹1.2L monthly revenue in 2–3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   The booking named turning visits into orders first, so the opening month on this smaller store goes there. Before budget rises, close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive. Set basket-size offers that reward a second and third item in the cart.

   Why

   A smaller electronics brand came to us to grow monthly revenue in 2–3 months, from ₹1L to ₹10L–₹15L+ (12.5x). That is faster than nine in ten of our measured accounts grew over the same time, so the plan below is modelled on what that fastest tenth reached and shows where it lands against the target. At booking, the brand named turning visits into orders as its main problem, with competition close behind. Every rupee of ads is capped by how well the store turns visits into orders. Each extra item in a basket is revenue the ad has already paid for.

   How it works

   The fix list goes to the brand's team in the opening weeks, ahead of any budget step. The tiers follow real order values, not round numbers.

2. 2

   Measurement & targets · Month 1 to 3

   What we'd do

   Keep a shared daily sheet with the ad platform's revenue next to real store orders. Set the path from ₹1L to ₹10L–₹15L+ as written monthly targets, and read every review against the month so far. Raise budget in a month only while return holds; where it would slip too far, hold it.

   Why

   No return on ad spend was given at booking; the starting return is what measured electronics stores of that size hold. Platform attribution over-counts, so budget decisions sit on the store-side number. A missed month shows up early instead of at the end of the plan.

   How it works

   The sheet is read before each budget change. Written targets make each scaling decision explicit. A month whose return would slip past that point keeps its budget instead.

3. 3

   Creative testing · Month 1

   What we'd do

   Test with video, static image and catalogue ads first, rising to about two dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. Run every lead product in a campaign of its own, read every week. Run creator, customer-feedback and founder-led video. Test several interest clusters against a broad audience, with video and catalogue ads.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Trust-carrying video held return in most measured accounts. Buyers split by use case, so clusters show which one buys before budget is committed.

   How it works

   A product that does not sell is paused inside the week. Weak UGC is swapped for founder-led video rather than scaled. Clusters are read against broad before the scaling plan is set. The number of new ads grows with spend; video makes up the largest part and static image the next.

4. 4

   Scaling · Month 2 to 3

   What we'd do

   Through Month 2 to Month 3, push toward ₹10L–₹15L+: the budget rises gently while return holds, and Facebook Feed carries the most spend and Instagram Reels the next. Refresh tired ads by mixing old and new creatives as spend rises. Plan spend against stock with a product-level forecast, and scale with bundle offers and a cost-control campaign.

   Why

   In Month 2 to Month 3 the modelled budget rises gently, while return on spend holds. Two of these months stop their budget step where return would slip too far. Each budget step here is sized so that return stays close to where it was. Frequency climbs with spend, and tired ads lose click-through first.

   How it works

   Proven ads stay while new ones are added. Spend is planned against stock by product. Facebook Feed carries the largest share of spend in the final month, with Instagram Reels next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. Cart-value offers go live at checkout. Store fixes go live: reviews, trust pointers and the prepaid offer.

   • Projected revenue ₹1L
   • Projected ROAS 1.92x
   • Planned ad spend ₹54,502
2. Month 2

   Scaling begins: bundle offers run with a cost-control campaign. Budget is raised only as far as return allows: return holds as the budget steps up.

   • Projected revenue ₹1.2L
   • Projected ROAS 1.95x
   • Planned ad spend ₹62,074
3. Month 3

   A fresh round of creator and customer-feedback video goes live. Budget is raised only as far as return allows: return holds as the budget holds. The plan finishes short of ₹10L–₹15L+: budget stops rising where return would slip. Cost per purchase ends close to the learning phase.

   • Projected revenue ₹1.2L
   • Projected ROAS 1.95x
   • Planned ad spend ₹63,951

1. 01

   Website fixes: shop-by-phone-brand menu, reviews and trust pointers on product pages

2. 02

   Ask for a product-level festive forecast so spend follows stock.

3. 03

   Kept a creatives bank and 4–8 social posts a month